Even then it’s questionable if it would be effective. Over time even a faint behavioral signature will become transparent, because things that deviate from the true behavioral signature do so in random ways, which could essentially “cancel” out if your model for quantifying behavioral characteristics is well-specified. Meanwhile the “true” behaviors would “add” over time.

It would become like any other signal jamming arms race, whether it’s radar or social behaviors, and your model of generating random noise has to get more sophisticated as the other party’s anti-jamming techniques get more sophisticated.

I took a class with Scott Aaronson once where he mentioned the idea that the natural enemy of machine learning is cryptography.

So if you know the anti-jammers are using ever greater machine learning techniques, rather than trying to one-up them with adversarial learning, I suspect the best jamming would be cryptography.

Like, extensions to Facebook that essential encode text with PGP or something, send via Messenger, and allow decoding on the other side.

Then an interesting idea for machine learning would be how to make an autoencoder that accepts encrypted text, transforms it into human understandable text that would fool a machine learning algorithm designed to flag encrypted text, and can decode from natural language back to the encrypted data on the other end.

> I took a class with Scott Aaronson once where he mentioned the idea that the natural enemy of machine learning is cryptography.

He cites Rivest for that one: https://people.csail.mit.edu/rivest/pubs/Riv91.pdf